A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Improves accuracy of SMCI estimators without expanding sum regions.
Neural network accuracy improves with denser training samples.
SVM predicts regional rainfall with varying accuracy, best in central US.
RAMs improve GAMs' accuracy by fitting components to subregions of feature space.
Proposes a method to estimate acceptance regions for many classes, including new ones.
The study examines the properties of linear regions in DNNs and how optimization techniques affect them.
BO limits search to lower dimensions with LGPR, improving efficiency and accuracy.
New sampling strategy improves TR algorithms for stochastic optimization.
Efficiently estimates material parameter space with multifidelity Gaussian process modeling.
Art historians and archaeologists have long grappled with the regional classification of ancient Near Eastern ivory carvings. Based on the visual similarity of sculptures, individuals within these fields have proposed object assemblages linked to hypothesized regional production centers. Using quantitative rather than …
Neural Architecture Search (NAS) has emerged as a promising technique for automatic neural network design. However, existing MCTS based NAS approaches often utilize manually designed action space, which is not directly related to the performance metric to be optimized (e.g., accuracy), leading to sample-inefficient exp…
Method uses aggregate crop statistics to improve satellite-based crop type mapping.
Hidden Markov Models detect hand gestures from wearable sEMG signals.
Model learns image-word associations from captions using contrastive learning.
fMRI semantic category understanding using linguistic encoding models attempt to learn a forward mapping that relates stimuli to the corresponding brain activation. Classical encoding models use linear multi-variate methods to predict the brain activation (all voxels) given the stimulus. However, these methods essentia…
The lack of interpretability remains a barrier to the adoption of deep neural networks. Recently, tree regularization has been proposed to encourage deep neural networks to resemble compact, axis-aligned decision trees without significant compromises in accuracy. However, it may be unreasonable to expect that a single …
The study proposes a framework to accept OOD data based on competence scores.
Deep neural networks (DNNs) have transformed several artificial intelligence research areas including computer vision, speech recognition, and natural language processing. However, recent studies demonstrated that DNNs are vulnerable to adversarial manipulations at testing time. Specifically, suppose we have a testing …
MPSA-DenseNet improves accent classification accuracy.
We consider the problem of model selection in Gaussian Markov fields in the sample deficient scenario. The benchmark information-theoretic results in the case of d-regular graphs require the number of samples to be at least proportional to the logarithm of the number of vertices to allow consistent graph recovery. When…
Objective The electrical characteristics of the EEG signals can be used for seizure detection. Statistical independence between different brain regions is measured by functional brain connectivity (FBC). Specific directional effects can't consider by FBC and thus effective brain connectivity (EBC) is used to measure ca…
Enhances content moderation with culturally-aware models.
PR-GNN identifies salient brain regions for ASD biomarkers.
Inspired by the observation that humans are able to process videos efficiently by only paying attention where and when it is needed, we propose an interpretable and easy plug-in spatial-temporal attention mechanism for video action recognition. For spatial attention, we learn a saliency mask to allow the model to focus…
Bayesian deep learning improves accuracy and calibration without sacrificing scalability.
fMRI semantic category understanding using linguistic encoding models attempts to learn a forward mapping that relates stimuli to the corresponding brain activation. State-of-the-art encoding models use a single global model (linear or non-linear) to predict brain activation given the stimulus. However, the critical as…
This study presents an extension of the Gaussian process regression model for multiple-input multiple-output forecasting. This approach allows modelling the cross-dependencies between a given set of input variables and generating a vectorial prediction. Making use of the existing correlations in international tourism d…
The paper analyzes the tradeoffs between accuracy and invariance in learning representations.
The use of EEG biometrics, for the purpose of automatic people recognition, has received increasing attention in the recent years. Most of current analysis rely on the extraction of features characterizing the activity of single brain regions, like power-spectrum estimates, thus neglecting possible temporal dependencie…
Study how neural networks optimize to stable linearly connected regions.
The volume of stroke lesion is the gold standard for predicting the clinical outcome of stroke patients. However, the presence of stroke lesion may cause neural disruptions to other brain regions, and these potentially damaged regions may affect the clinical outcome of stroke patients. In this paper, we introduce the t…
Incorporating human domain knowledge for breast tumor diagnosis is challenging, since shape, boundary, curvature, intensity, or other common medical priors vary significantly across patients and cannot be employed. This work proposes a new approach for integrating visual saliency into a deep learning model for breast t…
New algorithm achieves strong consistency in binary non-uniform hypergraph classification.
Adaptive PINNs improve accuracy by adding points where solutions are uncertain.
Mapper-GIN simplifies 3D point cloud classification with lightweight structure.
New method detects and locates changes in spatio-temporal point processes.
Adaptive replication improves stochastic function optimization.
Event-based models (EBM) are a class of disease progression models that can be used to estimate temporal ordering of neuropathological changes from cross-sectional data. Current EBMs only handle scalar biomarkers, such as regional volumes, as inputs. However, regional aggregates are a crude summary of the underlying hi…
CW-EDMD improves prediction accuracy by learning local Koopman models for different state-space regions.
Functional connections in the brain are frequently represented by weighted networks, with nodes representing locations in the brain, and edges representing the strength of connectivity between these locations. One challenge in analyzing such data is that inference at the individual edge level is not particularly biolog…
Gradient flow on ReLU networks converges to a simple model with few regions.
ACE improves GFlowNet exploration efficiency by balancing complementary search strategies.
ANODE uses neural density estimation for anomaly detection in physics.
This research optimizes energy consumption forecasting in Puno using parallel computing and ARIMA models.
In the knowledge that the ex-post performance of Markowitz efficient portfolios is inferior to that implied ex-ante, we make two contributions to the portfolio selection literature. Firstly, we propose a methodology to identify the region of risk-expected return space where ex-post performance matches ex-ante estimates…
When approximating a black-box function, sampling with active learning focussing on regions with non-linear responses tends to improve accuracy. We present the FLOLA-Voronoi method introduced previously for deterministic responses, and theoretically derive the impact of output uncertainty. The algorithm automatically p…
This paper introduces channel gating, a dynamic, fine-grained, and hardware-efficient pruning scheme to reduce the computation cost for convolutional neural networks (CNNs). Channel gating identifies regions in the features that contribute less to the classification result, and skips the computation on a subset of the …
New method solves optimization problems with stochastic objectives and constraints.